Innovations in the Field

Sponsored by BASF

This yearlong endeavor looks at how four farmers are evaluating technology and agronomic information that can boost the productivity of their operations.

Different Latitude, Same Attitude

Ben Pederson

To learn how Ben plans to use management zones, read Bens's Blog.

Roughly 400 miles to the north, the climate is different, the soils are different, but the precision ag challenge is the same. Ben Pederson, Lake Mills, Iowa, transitioned from what he calls "casual" data gathering that started in the mid–90s to a more intensive commitment in 2000.

And what the data tells him again and again is that variability in the fields where he produces corn and soybeans with his father, Gary, is dragging productivity.

Initially, the Pedersons addressed the well–documented variance in production potential through grid sampling, but Ben was dissatisfied with the concept.

"Those 2.5–acre grids were the basis for our variable–rate fertilizer program, but there are no perfect squares in nature, and I don’t want to manage dissimilar elements in a similar manner," he says.

Now he’s fine–tuning management zones to address the spatial variability of soil properties. Historical yield data provides the basis for the zones, none of which exceeds 3 acres. Zones are soil–sampled, and Pederson considers the underlying soil chemistry in their establishment. The result is a more finely tuned variable–rate nutrient strategy, as well as a seeding prescription that better reflects environmental productivity.

"We have a long way to go, and we'll keep fine–tuning the zones and how we manage them based on the information we gather, but I'm happy we're on that path," Pederson says.

A vSet Select dual–hybrid planter will also allow the Pedersons to take precision planting a step beyond seeding rates.

"It's a very powerful concept–change hybrids on the fly to better match your seed genetics to the yield environment as those environments change within a field," Pederson says. "It's ideal for me because of the variability we have. If I have accurate field information, the ability to take advantage of better–suited hybrids within the field could mean 5, 10, maybe 20 bushels per acre."

Pederson is also upping the data bar. He has connected to the Farmers Business Network, a "big data" firm that analyzes information from millions of acres to provide a higher level of input insight. It's a move Missouri's Steve Cubbage has also made.

"It's the next step," Pederson observes. "We can put big data analytics to work for us–hybrid selection, seeding rates, who knows? The more data, the higher the degree of accuracy, and we're basing some very important decisions on data–as well as our own knowledge and experience. So the better that information is, the better our decisions are going to be."


Back in Missouri, Cubbage found the need for immediate data this summer. Far too much rain had fallen far too fast. He knew he had lost nitrogen (N). The question was, how much?

His BASF Innovation Specialist Kaleb Hellwig provided the answer. Using a Soil Scan 360 soil nitrate tester, Hellwig took soil samples from 0 to 12 inches and from 12 to 24 inches to determine how much nitrogen was left.

"I took hundreds of samples, not only for Steve and the guys but also on other farms in western Missouri," he notes. "In general, upland fields had lost 40 to 50 pounds of N–bottomland about double that. We found that some nitrogen had leached into the soil, but the biggest problem was denitrification. The ground was saturated for so long, the nitrogen turned into gas and volatilized."

Quantifying the available nitrogen facilitated accurate rates, and the test results could be acted upon immediately. Since Cubbage's team was sidedressing that day, it was simply a matter of adjusting the rate.

"The timeliness was important," Hellwig says. "Most of the corn I tested was V–7 to V–11, so for some of it, any longer and they wouldn't have been able to use a ground application rig."


Colorado State University soil scientist Dr. Rajiv Khosla focuses on helping growers more accurately delineate site–specific management zones. It's clear that a very high degree of variability exists in most fields, the precision agriculture researcher asserts. The challenge, he notes, is where to draw the lines.

"Is the soil the same 2 feet away from where you sampled? How about 10 yards—20, 30, 100 yards?" Khosla asks. "Clearly, the farther away you get, you're decreasing the chances that the soil properties are homogenous. You can only interpolate what is spatially dependent—locations that are alike in terms of whatever you're chasing. There's nothing wrong with grid sampling if you do it on the right scale. In my research, however, it is highly unlikely that data collected in a 2.5–acre grid would be spatially dependent.

"I think in terms of identifying management zones within which you can treat that area in a precise manner to optimize productivity."

But how do you identify those unique field areas? Several years ago, Khosla and his colleagues at Colorado State came up with a simple approach to a very complex challenge by merging three data layers:

  1. bare soil imagery to identify organic matter distribution
  2. topographical maps or images to reveal slopes, summits and other land features that have a relationship to yield; and
  3. the farmer's experience—his intimate knowledge of areas where production is particularly good or poor.

Since the time Khosla developed the approach, far more data has become available. Today, yield map data can be included along with soil information, electrical conductivity data or anything else that helps complete the productivity picture. Merging the data layers and analyzing them statistically provides the opportunity to utilize variable–rate technology in response to intrafield variations in soil and land properties.

Khosla cautions, however, that yield data, in particular, must have historical validity.

"You need seven or more years of data to accurately capture seasonal variability," he emphasizes.

Plenty of both human and technical resources are available to carry out the complex formulas and geostatistical number–crunching necessary to translate data into useful management information, Khosla says. There is, however, one very important prerequisite.

"You must have the data," he emphasizes. "You must have accurate data and an adequate history of data to make the conclusions stable over time. Without good data, you can't capture the macrovariability of a field—you'll always be overfertilizing one area and underfertilizing another, and you'll never achieve the true productive capability of your land."